Ngram 1/try llm code speedup #90
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Several did not work (which is ok), but to do further testing, needed to install more-itertools in venv, and can rerun some code
Forced restart cleared jupyter kernel, so might as well add FlagEmbedding library to create embeddings for retrieval. Read a few discussions on how slow Stanza is with the pipeline. One thing is that I think there is a strong benefit to using lemmas for tokens, which needs many of the processing pipelines. Unfortunately, this looks like the slow part of processing. Saw this discussion on [using multiprocessing combined with Stanza](stanfordnlp/stanza#552) and it seems like multiprocessing with GPU doesn't result in much performance increase but does drastically cause the GPU to work harder. However, discussion reminded me that I turned off GPU with Stanza in an attempt to use BERTopic with Stanza. Will re-enable and retime.
Moving lemmafication out of sklearn pipeline to speed up count vectorization/TFIDF Will have to add similar refactor to query code
Running into an error: ```TypeError: CustomSKLearnAnalyzer.ngrams_maker() got multiple values for argument 'min_ngram_length'```
Experiment with BGE-M3 and smaller datasets as laptop builds model(s)
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